Seth Spielman, University of Colorado, Boulder
CCPR Seminar Room 4240 Public Affairs Building, Los Angeles"Measuring neighborhoods in the new data economy."
Abstract: The data economy in the United States has changed dramatically in the past 5-10 years. Naively one might argue that this new data economy holds particular promise for academics, because revolutions in science are often preceded by revolutions in measurement. But for social scientists who study cities in the United States these changes are mixed. The new data economy is complex complicates the study of neighborhoods. In this talk I'll describe one such complication - the replacement of the long form of the decennial census with the American Community Survey in 2010. The ACS produces estimates for thousands of variables at a variety of geographic scales. However, estimates from the ACS are terribly imprecise, for many policy relevant variables ACS estimates are almost unusable. In this talk I’ll describe the quality of the ACS and use its shortcomings to motivate a discussion of changing the way we measure neighborhoods. Rather than just talk about alternatives I’ll present results from two novel computational methods that leverage new ways of thinking about the measurement of neighborhoods. One of these methods can be used to process existing public domain ACS estimates to dramatically reduce the margin of error.